import argparse
import os

import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed

from accelerate import Accelerator, DistributedType


########################################################################
# This is a fully working simple example to use Accelerate,
# specifically showcasing the experiment tracking capability,
# and builds off the `nlp_example.py` script.
#
# This example trains a Bert base model on GLUE MRPC
# in any of the following settings (with the same script):
#   - single CPU or single GPU
#   - multi GPUS (using PyTorch distributed mode)
#   - (multi) TPUs
#   - fp16 (mixed-precision) or fp32 (normal precision)
#
# To help focus on the differences in the code, building `DataLoaders`
# was refactored into its own function.
# New additions from the base script can be found quickly by
# looking for the # New Code # tags
#
# To run it in each of these various modes, follow the instructions
# in the readme for examples:
# https://github.com/huggingface/accelerate/tree/main/examples
#
########################################################################

_UpperCAmelCase          =						16
_UpperCAmelCase          =						32




def 							UpperCamelCase		(			__lowercase		:					Accelerator		,__lowercase		:					int = 16      ):
		'''simple docstring'''




		A_						:							List[str]      = AutoTokenizer.from_pretrained('bert-base-cased'      )
		A_						:							List[Any]      = load_dataset('glue'		,'mrpc'      )

		def tokenize_function(__lowercase		:					str      ):
				# max_length=None => use the model max length (it's actually the default)
				A_						:							List[str]      = tokenizer(examples['sentence1']		,examples['sentence2']		,truncation=__lowercase		,max_length=__lowercase      )
				return outputs

		# Apply the method we just defined to all the examples in all the splits of the dataset
		# starting with the main process first:
		with accelerator.main_process_first():
				A_						:							Tuple      = datasets.map(
				    __lowercase		,batched=__lowercase		,remove_columns=['idx', 'sentence1', 'sentence2']		,)

		# We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
		# transformers library
		A_						:							List[str]      = tokenized_datasets.rename_column('label'		,'labels'      )

		def collate_fn(__lowercase		:					Dict      ):
				# On TPU it's best to pad everything to the same length or training will be very slow.
				A_						:							Any      = 1_28 if accelerator.distributed_type == DistributedType.TPU else None
				# When using mixed precision we want round multiples of 8/16
				if accelerator.mixed_precision == "fp8":
						A_						:							Union[str, Any]      = 16
				elif accelerator.mixed_precision != "no":
						A_						:							Any      = 8
				else:
						A_						:							Optional[int]      = None

				return tokenizer.pad(
				    __lowercase		,padding='longest'		,max_length=__lowercase		,pad_to_multiple_of=__lowercase		,return_tensors='pt'		,)

		# Instantiate dataloaders.
		A_						:							Optional[Any]      = DataLoader(
		    tokenized_datasets['train']		,shuffle=__lowercase		,collate_fn=__lowercase		,batch_size=__lowercase      )
		A_						:							List[Any]      = DataLoader(
		    tokenized_datasets['validation']		,shuffle=__lowercase		,collate_fn=__lowercase		,batch_size=__lowercase      )

		return train_dataloader, eval_dataloader


# For testing only
if os.environ.get("""TESTING_MOCKED_DATALOADERS""", None) == "1":
	from accelerate.test_utils.training import mocked_dataloaders

	_UpperCAmelCase          =						mocked_dataloaders  # noqa: F811




def 							UpperCamelCase		(			__lowercase		:					Any		,__lowercase		:					Any      ):
		'''simple docstring'''




		if os.environ.get('TESTING_MOCKED_DATALOADERS'		,__lowercase      ) == "1":
				A_						:							Union[str, Any]      = 2
		# Initialize Accelerator

		# New Code #
		# We pass in "all" to `log_with` to grab all available trackers in the environment
		# Note: If using a custom `Tracker` class, should be passed in here such as:
		# >>> log_with = ["all", MyCustomTrackerClassInstance()]
		if args.with_tracking:
				A_						:							List[str]      = Accelerator(
				    cpu=args.cpu		,mixed_precision=args.mixed_precision		,log_with='all'		,project_dir=args.project_dir      )
		else:
				A_						:							Tuple      = Accelerator(cpu=args.cpu		,mixed_precision=args.mixed_precision      )
		# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
		A_						:							Optional[int]      = config['lr']
		A_						:							List[Any]      = int(config['num_epochs']      )
		A_						:							int      = int(config['seed']      )
		A_						:							Optional[int]      = int(config['batch_size']      )
		set_seed(__lowercase      )

		A_  ,     A_						:							Tuple      = get_dataloaders(__lowercase		,__lowercase      )
		A_						:							Any      = evaluate.load('glue'		,'mrpc'      )

		# If the batch size is too big we use gradient accumulation
		A_						:							List[Any]      = 1
		if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:
				A_						:							Union[str, Any]      = batch_size // MAX_GPU_BATCH_SIZE
				A_						:							int      = MAX_GPU_BATCH_SIZE

		# Instantiate the model (we build the model here so that the seed also control new weights initialization)
		A_						:							str      = AutoModelForSequenceClassification.from_pretrained('bert-base-cased'		,return_dict=__lowercase      )

		# We could avoid this line since the accelerator is set with `device_placement=True` (default value).
		# Note that if you are placing tensors on devices manually, this line absolutely needs to be before the optimizer
		# creation otherwise training will not work on TPU (`accelerate` will kindly throw an error to make us aware of that).
		A_						:							List[Any]      = model.to(accelerator.device      )

		# Instantiate optimizer
		A_						:							Union[str, Any]      = AdamW(params=model.parameters()		,lr=__lowercase      )

		# Instantiate scheduler
		A_						:							List[str]      = get_linear_schedule_with_warmup(
		    optimizer=__lowercase		,num_warmup_steps=1_00		,num_training_steps=(len(__lowercase      ) * num_epochs) // gradient_accumulation_steps		,)

		# Prepare everything
		# There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the
		# prepare method.
		A_  ,     A_  ,     A_  ,     A_  ,     A_						:							List[str]      = accelerator.prepare(
		    __lowercase		,__lowercase		,__lowercase		,__lowercase		,__lowercase      )

		# New Code #
		# We need to initialize the trackers we use. Overall configurations can also be stored
		if args.with_tracking:
				A_						:							Optional[int]      = os.path.split(__lowercase      )[-1].split('.'      )[0]
				accelerator.init_trackers(__lowercase		,__lowercase      )

		# Now we train the model
		for epoch in range(__lowercase      ):
				model.train()
				# New Code #
				# For our tracking example, we will log the total loss of each epoch
				if args.with_tracking:
						A_						:							Any      = 0
				for step, batch in enumerate(__lowercase      ):
						# We could avoid this line since we set the accelerator with `device_placement=True`.
						batch.to(accelerator.device      )
						A_						:							Dict      = model(**__lowercase      )
						A_						:							Union[str, Any]      = outputs.loss
						# New Code #
						if args.with_tracking:
								total_loss += loss.detach().float()
						A_						:							Optional[int]      = loss / gradient_accumulation_steps
						accelerator.backward(__lowercase      )
						if step % gradient_accumulation_steps == 0:
								optimizer.step()
								lr_scheduler.step()
								optimizer.zero_grad()

				model.eval()
				for step, batch in enumerate(__lowercase      ):
						# We could avoid this line since we set the accelerator with `device_placement=True` (the default).
						batch.to(accelerator.device      )
						with torch.no_grad():
								A_						:							List[Any]      = model(**__lowercase      )
						A_						:							Tuple      = outputs.logits.argmax(dim=-1      )
						A_  ,     A_						:							Any      = accelerator.gather_for_metrics((predictions, batch['labels'])      )
						metric.add_batch(
						    predictions=__lowercase		,references=__lowercase		,)

				A_						:							List[Any]      = metric.compute()
				# Use accelerator.print to print only on the main process.
				accelerator.print(f'''epoch {epoch}:'''		,__lowercase      )

				# New Code #
				# To actually log, we call `Accelerator.log`
				# The values passed can be of `str`, `int`, `float` or `dict` of `str` to `float`/`int`
				if args.with_tracking:
						accelerator.log(
						    {
						        'accuracy': eval_metric['accuracy'],
						        'f1': eval_metric['f1'],
						        'train_loss': total_loss.item() / len(__lowercase      ),
						        'epoch': epoch,
						    }		,step=__lowercase		,)

    # New Code #
    # When a run is finished, you should call `accelerator.end_training()`
    # to close all of the open trackers
		if args.with_tracking:
				accelerator.end_training()




def 							UpperCamelCase		(			):
		'''simple docstring'''




		A_						:							Any      = argparse.ArgumentParser(description='Simple example of training script.'      )
		parser.add_argument(
		    '--mixed_precision'		,type=__lowercase		,default=__lowercase		,choices=['no', 'fp16', 'bf16', 'fp8']		,help='Whether to use mixed precision. Choose'
		    'between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10.'
		    'and an Nvidia Ampere GPU.'		,)
		parser.add_argument('--cpu'		,action='store_true'		,help='If passed, will train on the CPU.'      )
		parser.add_argument(
		    '--with_tracking'		,action='store_true'		,help='Whether to load in all available experiment trackers from the environment and use them for logging.'		,)
		parser.add_argument(
		    '--project_dir'		,type=__lowercase		,default='logs'		,help='Location on where to store experiment tracking logs` and relevent project information'		,)
		A_						:							Dict      = parser.parse_args()
		A_						:							Dict      = {'lr': 2e-5, 'num_epochs': 3, 'seed': 42, 'batch_size': 16}
		training_function(__lowercase		,__lowercase      )


if __name__ == "__main__":
	main()







from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


_UpperCAmelCase          =						logging.get_logger(__name__)

_UpperCAmelCase          =						{
    """xlm-roberta-base""": """https://huggingface.co/xlm-roberta-base/resolve/main/config.json""",
    """xlm-roberta-large""": """https://huggingface.co/xlm-roberta-large/resolve/main/config.json""",
    """xlm-roberta-large-finetuned-conll02-dutch""": (
        """https://huggingface.co/xlm-roberta-large-finetuned-conll02-dutch/resolve/main/config.json"""
    ),
    """xlm-roberta-large-finetuned-conll02-spanish""": (
        """https://huggingface.co/xlm-roberta-large-finetuned-conll02-spanish/resolve/main/config.json"""
    ),
    """xlm-roberta-large-finetuned-conll03-english""": (
        """https://huggingface.co/xlm-roberta-large-finetuned-conll03-english/resolve/main/config.json"""
    ),
    """xlm-roberta-large-finetuned-conll03-german""": (
        """https://huggingface.co/xlm-roberta-large-finetuned-conll03-german/resolve/main/config.json"""
    ),
}





class UpperCAmelCase		(   __A    ):


				'''simple docstring'''






				lowerCamelCase_        =   '''xlm-roberta'''
				def __init__(		self						,      lowercase=3_0_5_2_2						,      lowercase=7_6_8						,      lowercase=1_2						,      lowercase=1_2						,      lowercase=3_0_7_2						,      lowercase="gelu"						,      lowercase=0.1						,      lowercase=0.1						,      lowercase=5_1_2						,      lowercase=2						,      lowercase=0.02						,      lowercase=1E-12						,      lowercase=1						,      lowercase=0						,      lowercase=2						,      lowercase="absolute"						,      lowercase=True						,      lowercase=None						,      **lowercase						,      ):



						"""simple docstring"""





						super().__init__(pad_token_id=lowercase						,      bos_token_id=lowercase						,      eos_token_id=lowercase						,      **lowercase   )

						A_						:							Union[str, Any]      = vocab_size
						A_						:							int      = hidden_size
						A_						:							Dict      = num_hidden_layers
						A_						:							List[str]      = num_attention_heads
						A_						:							Tuple      = hidden_act
						A_						:							Optional[int]      = intermediate_size
						A_						:							Any      = hidden_dropout_prob
						A_						:							Tuple      = attention_probs_dropout_prob
						A_						:							int      = max_position_embeddings
						A_						:							Union[str, Any]      = type_vocab_size
						A_						:							List[str]      = initializer_range
						A_						:							Tuple      = layer_norm_eps
						A_						:							str      = position_embedding_type
						A_						:							Any      = use_cache
						A_						:							str      = classifier_dropout





class UpperCAmelCase		(   __A    ):


				'''simple docstring'''






				@property
				def     lowerCAmelCase_  (		self   ):



						"""simple docstring"""





						if self.task == "multiple-choice":
								A_						:							Union[str, Any]      = {0: 'batch', 1: 'choice', 2: 'sequence'}
						else:
								A_						:							str      = {0: 'batch', 1: 'sequence'}
						return OrderedDict(
						    [
						        ('input_ids', dynamic_axis),
						        ('attention_mask', dynamic_axis),
						    ]   )






